Precision of polarimetric orthogonal state contrast estimation in coherent images corrupted by speckle, Poisson, and additive noise
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Bibliographic record
Abstract
We consider laser-illuminated active polarimetric imaging systems that measure the orthogonal state contrast (OSC), a frequently used surrogate to the degree of polarization, that can be used, for example, to discriminate manmade objects from natural backgrounds in remote sensing. We investigate the estimation precision of the OSC parameter in the presence of speckle, Poisson, and additive noise by using the Cramer-Rao lower bound (CRLB). Using Monte Carlo simulations and optical experiments, we show that the expression of the CRLB models the actual OSC estimation performance with excellent accuracy. This result is important for the design and sizing of active polarimetric imagers since the closed-form expression of the CRLB makes it handy for back-of-the-envelope calculations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it